Studying the effects of carbon prices on sustainable development in British Columbia
Bibliographic record
Abstract
As the years have advanced, politicians have increasingly discussed the effectiveness and rightness of putting prices on greenhouse gas emissions to discourage people from contributing more to the problem of climate change. British Columbia (B.C.), Canada’s westernmost province, introduced one such price in mid-2008 in the form of a carbon tax shift, which offset revenue that accrued to the provincial government from the sale of many carbon-based fuels with consequent cuts in personal and corporate income taxes. The provincial government argued that this would achieve greater economic growth while reducing greenhouse gas emissions and any resulting damage to the environment; however, many British Columbians have opposed the policy since it was introduced, especially in rural B.C. In this thesis, I examine the efficacy of B.C.’s carbon tax by comparing its performance on indicators associated with Sustainable Development Goals 8 (to achieve decent work and economic growth), 10 (to reduce inequality), 12 (to make production and consumption sustainable), and 13 (to reduce the damages of climate change), before and after the carbon tax was introduced, from the years 2001 to 2015, using a simple linear model to estimate the direction, magnitude, and statistical significance of changes associated with introducing the carbon tax in 2008 and raising it each year until 2012. I also conducted remote telephone interviews with randomly selected British Columbians (10 December 2020 – 18 March 2021, n=24), eight each in the communities of Coquitlam, Elkford, and Smithers, to ask them what they thought about the carbon tax’s effect on their lives, especially their ability to act more sustainably. While I found mainly mixed to positive effects associated with the carbon tax on B.C.’s aggregate-level performance with the Sustainable Development Goal indicators, most of the individuals I interviewed said the tax had not met with such success since then and, in fact, said it was responsible for making their lives worse. I find that many of the people I interviewed felt excluded from the process of making a policy that affected and continues to affect them, and that this alienation was strongest in the rural coal-mining community of Elkford. I suggest that the B.C. government should redesign its carbon tax with extensive public participation and focus on reducing greenhouse gas emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".